Chemical Exposome Research Articles

The Pregnancy Chemisome in Relation to Birth Outcomes and Consumer Product Use: Suspect Screening of Industrial Chemicals

Our study aims to characterize the chemical exposome among a diverse cohort of pregnant women and examine their relationship with birth outcomes, demographics, and consumer product use.We used liqu...

Induction of Amyloid-β42 Production by Fipronil and Other Pyrazole Insecticides.

Generation of amyloid-β peptides (Aβs) by proteolytic cleavage of the amyloid-β protein precursor (AβPP), especially increased production of Aβ42/Aβ43 over Aβ40, and their aggregation as oligomers and plaques, represent a characteristic feature of Alzheimer's disease (AD). In familial AD (FAD), altered Aβ production originates from specific mutations ofAβPP or presenilins 1/2 (PS1/PS2), the catalytic subunits of γ-secretase. In sporadic AD, the origin of altered production of Aβs remains unknown. We hypothesize that the 'human chemical exposome' contains products able to favor the production of Aβ42/Aβ43 over Aβ40 and shorter Aβs. To detect such products, we screened a library of 3500 + compounds in a cell-based assay for enhanced Aβ42/Aβ43 production. Nine pyrazole insecticides were found to induce a β- and γ-secretase-dependent, 3-10-fold increase in the production of extracellular Aβ42 in various cell lines and neurons differentiated from induced pluripotent stem cells derived from healthy and FAD patients. Immunoprecipitation/mass spectrometry analyses showed increased production of Aβs cleaved at positions 42/43, and reduced production of peptides cleaved at positions 38 and shorter. Strongly supporting a direct effect on γ-secretase activity, pyrazoles shifted the cleavage pattern of another γ-secretase substrate, alcadeinα, and shifted the cleavage of AβPP by highly purified γ-secretase toward Aβ42/Aβ43. Focusing on fipronil, we showed that some of its metabolites, in particular the persistent fipronil sulfone, also favor the production of Aβ42/Aβ43 in both cell-based and cell-free systems. Fipronil administered orally to mice and rats is known to be metabolized rapidly, mostly to fipronil sulfone, which stably accumulates in adipose tissue and brain. In conclusion, several widely used pyrazole insecticides enhance the production of toxic, aggregation prone Aβ42/Aβ43 peptides, suggesting the possible existence of environmental "Alzheimerogens" which may contribute to the initiation and propagation of the amyloidogenic process in sporadic AD.

Specific Triazine Herbicides Induce Amyloid-β42 Production.

Proteolytic cleavage of the amyloid-β protein precursor (AβPP) by secretases leads to extracellular release of amyloid-β (Aβ) peptides. Increased production of Aβ42 over Aβ40 and aggregation into oligomers and plaques constitute an Alzheimer's disease (AD) hallmark. Identifying products of the 'human chemical exposome' (HCE) able to induce Aβ42 production may be a key to understanding some of the initiating causes of AD and to generate non-genetic, chemically-induced AD animal models. A cell model was used to screen HCE libraries for Aβ42 inducers. Out of 3500+ compounds, six triazine herbicides were found that induced a β- and γ-secretases-dependent, 2-10 fold increase in the production of extracellular Aβ42 in various cell lines, primary neuronal cells, and neurons differentiated from human-induced pluripotent stem cells (iPSCs). Immunoprecipitation/mass spectrometry analyses show enhanced production of Aβ peptides cleaved at positions 42/43, and reduced production of peptides cleaved at positions 38 and lower, a characteristic of AD. Neurons derived from iPSCs obtained from a familial AD (FAD) patient (AβPP K724N) produced more Aβ42 versus Aβ40 than neurons derived from healthy controls iPSCs (AβPP WT). Triazines enhanced Aβ42 production in both control and AD iPSCs-derived neurons. Triazines also shifted the cleavage pattern of alcadeinα, another γ-secretase substrate, suggesting a direct effect of triazines on γ-secretase activity. In conclusion, several widely used triazines enhance the production of toxic, aggregation prone Aβ42/Aβ43 amyloids, suggesting the possible existence of environmental "Alzheimerogens" which may contribute to the initiation and propagation of the amyloidogenic process in late-onset AD.

Microbiome-Linked Crosstalk in the Gastrointestinal Exposome towards Host Health and Disease

The gastrointestinal exposome represents the integration of all xenobiotic components and host-derived endogenous components affecting the host health, disease progression and ultimately clinical outcomes during the lifespan. The human gut microbiome as a dynamic exposome of commensalism continuously interacts with other exogenous exposome as well as host sentineling components including the immune and neuroendocrine circuit. The composition and diversity of the microbiome are established on the basis of the luminal environment (physical, chemical and biological exposome) and host surveillance at each part of the gastrointestinal lining. Whereas the chemical exposome derived from nutrients and other xenobiotics can influence the dynamics of microbiome community (the stability, diversity, or resilience), the microbiomes reciprocally alter the bioavailability and activities of the chemical exposome in the mucosa. In particular, xenobiotic metabolites by the gut microbial enzymes can be either beneficial or detrimental to the host health although xenobiotics can alter the composition and diversity of the gut microbiome. The integration of the mucosal crosstalk in the exposome determines the fate of microbiome community and host response to the etiologic factors of disease. Therefore, the network between microbiome and other mucosal exposome would provide new insights into the clinical intervention against the mucosal or systemic disorders via regulation of the gut-associated immunological, metabolic, or neuroendocrine system.

An Integrated Approach to the Exposome

The editorial by Lioy and Rappaport (2011) provides a timely addition to the discussion about the exposome and exposure science. We are encouraged by the recognition of the importance of combining measurements of personal exposure with measurements of biological markers of exposure. However, rather than focusing on two approaches (i.e., top-down vs. bottom-up), we advocate a fully integrated approach to measurement of the exposome. There are currently serious limitations in measuring internal and external exposure. It may be feasible to measure biological markers in blood or other media periodically, but such measures are not without difficulties. Recent developments in omics technology are very promising, but many of these techniques have low reproducibility between laboratories, show high intraindividual variability, and are still expensive; in addition, uncertainties remain in biological interpretation of these markers (Vineis et al. 2009). It is still often impractical to prospectively measure personal inhalation, dermal, and ingestion exposure. Such information could be collected periodically, but the scientific effort would be great and the intrusion into the subjects’ lives would probably be unacceptable. Increased research effort will undoubtedly help improve measurement of both internal and external exposure. However, other sources of information exist that could contribute to constructing the exposome. We all routinely leave traces of our exposome in everyday electronic databases or databases that could be easily constructed. For example, the goods we purchase in a supermarket are often tracked by loyalty cards, which may provide a rich source of information on food consumption and the consumer products we use. Consumption of electricity and natural gas is increasingly being logged electronically by utility companies to assist billing. These data could be used to determine use of electrical items (informative about exposure to electric and magnetic fields) and activity patterns. It is relatively straightforward to track movements of individuals using mobile phones, and these data can be used, for example, to help estimate exposure to air pollutants. Within the next few years we will see an explosion in availability of sensors for many environmental contaminants that will be relatively cheap and easy to use and that could provide a more or less continuous log of information that can be related to exposure. These include simple sensors in the homes of subjects that continuously record information on air temperature, airborne contaminants, and other environmental factors. These sensors may provide personal exposure data or could, in combination with activity patterns and behavior, be used to reconstruct exposure profiles. The availability of data on use and activity patterns, as well as developments in sensor and omics technology, suggests that the dichotomy in top-down and bottom-up approaches may not be appropriate, as there are other strategies that could be used to determine the exposome. In addition, the terms “top-down” and “bottom-up” may be interpreted differently, with consequent confusion of terminology. Instead, we should aim to develop a concept of the exposome that takes into account all sources of available exposure information. The key factor in developing an integrated approach will be the articulation of clear theoretical paradigms linking exposures with disease. All of the exposure and contextual data could be used to reconstruct the exposome of individuals in an epidemiological study using appropriate models to link the data to parameters of interest in the exposome. Data on internal and external exposures, data on personal behavior, and environmental information from sensors could be used to triangulate on the exposome. This is an extremely exciting time in exposure science, and we believe that the coming years will provide a great opportunity to make a significant leap forward in understanding the relationship between environmental exposure and disease. Maximizing the opportunities provided by various developments requires a fully integrated approach to the exposome. This approach must be based on a clear theoretical framework that incorporates measurement and modeling of external exposure, databanks on patterns of behaviors, and markers of internal exposure.